Design and develop deep learning models for tasks such as image classification, object detection, speech recognition, and natural language processing.
Train, evaluate, and optimize neural networks using large-scale datasets and advanced techniques like transfer learning, data augmentation, and hyperparameter tuning.
Implement state-of-the-art deep learning architectures, including CNNs, RNNs, LSTMs, Transformers, GANs, and autoencoders.
Collaborate with cross-functional teams including data scientists, software engineers, and product managers to define and deliver AI-powered features and solutions.
Build and maintain scalable data pipelines for preprocessing, labeling, and augmentation of structured and unstructured data.
Conduct research and literature reviews to evaluate and implement the latest algorithms and advancements in deep learning.
Optimize model performance for deployment using techniques like quantization, pruning, and model compression.
Deploy models into production environments, ensuring performance, reliability, and scalability using cloud platforms (AWS, GCP, Azure) or on-edge devices.
Write clean, maintainable, and well-documented code that adheres to software engineering best practices.
Monitor and troubleshoot deployed models, tracking key performance metrics and retraining when necessary.
Ensure ethical AI practices, including fairness, explainability, and accountability in model development and deployment.
Stay up-to-date with the AI/ML community, contributing to internal knowledge-sharing sessions and possibly publishing research papers or blog posts.
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